Future biomarker uses might be more future-oriented

By Anette Breindl Science Editor

SAN DIEGO – The annual meeting of the American Association for Cancer Research (AACR) draws a broad mix of clinical and basic scientists, and so the gathering usually brings news of broad themes of cancer research – be they biomarkers, immunotherapies or clinical trial design – from both a theoretical and a practical standpoint. Biomarkers exemplified that dual strength this year.

As clinical results from trials like I-SPY 2 and PALOMA-1 were being presented at the meeting, researchers were also presenting theoretical ideas about how biomarkers might expand their capabilities.

A thought-provoking presentation came from Robert Beckman, a former Merck & Co. executive who is the founder of Onco-Mind llc. At a Sunday session on “Clinical Validation of Predictive Biomarkers,” Beckman argued that biomarkers could be used far more strategically than they are now.

Even useful and highly successful biomarkers, he argued, focus on the current molecular characteristics of a tumor, and those present at diagnosis, not on what he termed “the end game” – preventing resistance and ultimately curing the cancer.

And that, he said, is a mistake – because tumors are genetically unstable, and focusing on their current characteristics dooms clinicians to playing catch-up with a tumor that is always getting ahead of them.

“In chess, we have very complex strategies designed to win,” he told his audience. “With cancer patients our strategies aren’t as complex. . . . We individualize the therapy, but the strategy is always the same.”

It’s not surprising, he said, because cancer is a lot more complicated than chess. “There are so many thousand pieces that we are lucky if we can just identify them all.”

But tumors have more than one mutation, and are pretty much guaranteed to develop further mutations over the course of treatment – which is why so many targeted agents quickly succumb to resistance.

Beckman argued that ideally, biomarkers should be used to implement a data-driven strategy for planning a sequence of therapies, one whose goal is not just stopping the growth of the tumor in its current state, but also the prevention of resistance.

During a stint at the Institute for Advanced Studies, Beckman and his colleagues used mathematical modeling to test the effects of different treatment strategies on both the short-term and the long-term growth of tumors.

The result: Under certain circumstances, the overall optimal strategy is to first treat patients with a drug that does not halt tumor growth, but is best at killing tumors with mutations that would be likely to turn into instigators of relapse.

The result is counterintuitive, and would likely be hard to sell to many practicing oncologists. But Beckman said this is where the true potential of biomarkers lies.

Optimization of the treatment strategy, he said, needs to be for “the overall goal, not the current step.”

Meanwhile, clinical results presented at annual meeting illustrated that well-used biomarkers in clinical trials can already enable clinical trials whose scope and speed would not be possible without those markers.

Biomarkers are critical to the success of precision medicine. But one of the challenges of developing a biomarker is that often, a trial using such a biomarker is trying to answer two questions in one trial: one about the drug, and one about the biomarker itself.

The I-SPY 2 trial, which is simultaneously testing multiple breast cancer drugs for neoadjuvant use before surgery, has been noted for its innovative approach to solving this potential dilemma.

The strategy of I-SPY is to adaptively randomize patients to simultaneous phase II trials of several different targeted agents, based on eight biomarkers assembled into 10 clinically relevant signatures. The likelihood of being assigned to a given trial is not completely random, but also not completely determined by a patient’s gene signature.

Instead, that probability is tweaked on an ongoing basis as treatment results come in – an approach that allows patients to have a greater chance of being in groups they will likely benefit from based on their genomic profile, while allowing the discovery of any unexpected relationships that might exist between biomarkers and therapies.

That possibility of unexpected relationships is anything but academic. In one of the most high-profile trials presented this year at AACR, the PALOMA-1 trial of palbociclib plus letrozole in metastatic breast cancer, the molecular biomarker that was being tested for its ability to predict the response to palbociclib had a closer relationship with the response to letrozole instead. (See the article on p. 1.)

I-SPY 2 has everything that is supposed to save drug development; biomarkers, adaptive trial design, and, on the business side, a precompetitive public-private partnership between FDA, the National Institutes of Health, the Centers for Medicare & Medicaid Services, the Biotechnology Industry Organization, the Pharmaceutical Research and Manufacturers of America and drugmakers and nonprofits. (See BioWorld Today, March 18, 2010.)

So far, I-SPY 2 is living up to its expectations. At the conference, researchers “graduated” experimental HER inhibitor neratinib from its cohort, meaning that the drug has a high probability of demonstrating phase III efficacy in at least one subgroup.

Preliminary results from the trial were released last December. (SeeBioWorld Today, Dec. 6, 2013.)

For neratinib, the gene signature identified as associated with the best chances of success was HER-positive, hormone-receptor negative.

The successful graduate is under consideration for an I-SPY phase III trial, either in HER2+/ HR- patients – the signature identified in I-SPY 2 – or all HER2+ patients, a larger group in which the drug also had a good showing.